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# gauss256/cola_test.py Created May 5, 2018

Script to confirm that STFT can be invertible without the COLA constraint being satisfied
 """Framework to confirm that STFT is invertible even if window is not COLA For the theory behind this, see: https://gauss256.github.io/blog/cola.html""" import numpy as np from scipy import signal def stft(x, w, nperseg, nhop): """Forward STFT""" X = np.array( [np.fft.fft(w * x[i:i + nperseg]) for i in range(0, 1 + len(x) - nperseg, nhop)] ) return X def istft(X, w, nperseg, nhop): """Inverse STFT""" x = np.zeros(X.shape * nhop) wsum = np.zeros(X.shape * nhop) for n, i in enumerate(range(0, 1 + len(x) - nperseg, nhop)): x[i:i + nperseg] += np.real(np.fft.ifft(X[n])) * w wsum[i:i + nperseg] += w ** 2. pos = wsum != 0 x[pos] /= wsum[pos] return x def main(): """Change the parameters and window below to test other scenarios""" length = 4096 nperseg = 256 nhop = 64 noverlap = nperseg - nhop w = np.sqrt(signal.hann(nperseg)) pad = nperseg # The theory assumes that the signal has been zero-padded to infinity, # so some attention needs to be paid to the boundaries. The following # is not elegant but it serves the purpose. x = np.random.randn(length + 2 * pad) if pad > 0: x[:pad] = 0 x[-pad:] = 0 X = stft(x, w, nperseg, nhop) y = istft(X, w, nperseg, nhop)[pad:pad + length] x_hat = x[pad:pad + length] print(f"check_COLA: {signal.check_COLA(w, nperseg, noverlap)}") print(f"allclose : {np.allclose(x_hat, y)}") if __name__ == "__main__": main()
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### gauss256 commented May 5, 2018

 The window used is square root Hann. It is not COLA, as confirmed by `check_COLA`. But the STFT that uses it is invertible, as confirmed by `allclose`.